Investigation on the Impact of Coating Thickness Setting and Calendering on the NMC 811 Cathode Performances for Lithium-Ion Batteries
Bibliographic record
Abstract
The need for energy in various activities around the world is increasing.Batteries come as an important energy solution, among others, for various applications, such as electric vehicles, mass storage for utilities, gadgets, etc. Lithium-ion batteries that are able to produce superior performance are needed.The battery cathode is an important key to the performance of lithium-ion batteries.The battery cathode fabrication factor is one that affects the quality of the battery produced.The thickness of the coating material will determine the amount of active material contained in the cathode, while the calendering process is needed to compress the material in the cathode so that the lithium-ion transfer process in the battery can run more effectively.Based on the research results, the thicker the coating material on the cathode, the greater the capacity value produced.With a coating thickness of 300 µm, the resulting capacity reaches 180 mAh/gr.However, in the life cycle and rate capability tests, the performance stability is not better than the thickness of 100 µm and 200 µm.As for the calendering level, the battery with an optimum decrease in dry thickness is able to have the best performance in terms of capacity, life cycle, and rate capability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".